index

AshplotR

Introduction

This script can be used to create bi-plots and Harker plots of all tephra major elemental data. It includes interactive exploration of the data and some discussion on identifying useful elements for discrimination. Guidance for saving plots as .svg or .pdf files is also provided. All the code used to produce this document is downloadable as an RMarkdown file. To run this you will require r and RStudio. We are happy for you to use, share and improve our code, but would ask that you cite this as a poin tof origin for the plots.

Load packages

We first load in packages required to generate the plots and manipulate the data. You may need to install these packages in r or update them to work correctly.

library(ggplot2)
library(factoextra)
library(readr)
library(compositions)
library(gganimate)
library(cowplot)
library(tidyverse)
library(gt)
library(plotly)
library(svglite)
library(png)
library(here)
library(ggdensity)

Load your data

Any data placed within the the data file of the r project can be read using the code outlined below. As this is a Markdown script the ‘here’ package is used to ensure that project links do not break when moving scripts around. As long as you start a new R project and drop this script into it (using the structure outlined on the home page) then it will work. Data files should be in .csv format and contain the group of the tephra as column 1 under the title “id” (see example files for reference). Loaded here are some data for core ODP-980 in the North Atlantic and a terrestrial site from the UK called MT.

chem <- here::here("Data","Borrobol_type.csv") %>% read.csv()

Check your data has loaded correctly

The following script produces a summary table of mean values and number of analyses in your data.

chem_sum <- chem %>% group_by(id) %>% dplyr::summarise(                                            
                                                       count=n(),
                                                       SiO2.= mean(SiO2),
                                                       dev_1 = sd(SiO2),
                                                       TiO2. = mean(TiO2),
                                                       dev_2 = sd(TiO2),
                                                       Al2O3. = mean(Al2O3),
                                                       dev_3 = sd(Al2O3),
                                                       FeOt = mean(FeO),
                                                       dev_4 = sd(FeO),
                                                       MnO.= mean(MnO),
                                                       dev_5 = sd(MnO),
                                                       MgO.= mean(MgO),
                                                       dev_6 = sd(MgO),
                                                       CaO.= mean(CaO),
                                                       dev_7 = sd(CaO),
                                                       Na2O. = mean(Na2O),
                                                       dev_8 = sd(Na2O),
                                                       K2O. = mean(K2O),
                                                       dev_9 = sd(K2O),
                                                       P2O5. = mean(P2O5),
                                                       dev_10 = sd(P2O5),
                                                       Total.=mean(Total),
                                                       dev_11 = sd(Total))
chem_sum <- chem_sum %>% mutate(across(where(is.numeric), ~ round(., digits = 2)))
chem_sum %>% rename(layer = id, n = count , SiO2 = SiO2., sd1 = dev_1, TiO2 = TiO2. , sd2 = dev_2)

## # A tibble: 12 x 24
##    layer          n  SiO2   sd1  TiO2   sd2 Al2O3. dev_3  FeOt dev_4  MnO. dev_5
##    <chr>      <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 Borrobol-~     2  76.4  0.39  0.07  0.01   13.0  0.42  0.95  0.01  0.05  0.01
##  2 Borrobol-~    12  76.4  0.41  0.12  0.01   12.7  0.34  1.56  0.12  0.04  0.01
##  3 Borrobol-~     8  76.4  0.59  0.12  0.01   12.6  0.57  1.47  0.15  0.04  0.01
##  4 Borrobol-~    35  75.9  0.31  0.15  0.01   12.9  0.2   1.58  0.13  0.04  0.01
##  5 Borrobol ~   124  76.4  0.36  0.13  0.01   12.7  0.21  1.55  0.11  0.04  0.01
##  6 Borrobol ~    13  76.5  0.25  0.13  0.01   12.8  0.23  1.53  0.1   0.04  0.01
##  7 Borrobol ~    21  76.5  0.54  0.12  0.02   12.9  0.48  1.36  0.16  0.04  0.01
##  8 CRUM1 597~    28  76.7  0.27  0.13  0.01   12.6  0.21  1.53  0.11  0.04  0.01
##  9 Fosen Tep~    10  76.9  0.49  0.12  0.01   12.3  0.43  1.54  0.2   0.05  0   
## 10 Hasseldal~   104  77.8  0.5   0.09  0.02   12.2  0.32  1.14  0.14  0.04  0.02
## 11 Hovsdalur~    23  77.9  0.53  0.12  0.02   12.3  0.12  1.16  0.12  0.04  0.02
## 12 Penifiler~   171  76.8  0.49  0.12  0.02   12.8  0.31  1.36  0.2   0.04  0.02
## # ... with 12 more variables: MgO. <dbl>, dev_6 <dbl>, CaO. <dbl>, dev_7 <dbl>,
## #   Na2O. <dbl>, dev_8 <dbl>, K2O. <dbl>, dev_9 <dbl>, P2O5. <dbl>,
## #   dev_10 <dbl>, Total. <dbl>, dev_11 <dbl>

gt(chem_sum)

id	count	SiO2.	dev_1	TiO2.	dev_2	Al2O3.	dev_3	FeOt	dev_4	MnO.	dev_5	MgO.	dev_6	CaO.	dev_7	Na2O.	dev_8	K2O.	dev_9	P2O5.	dev_10	Total.	dev_11
Borrobol-type Tephra GI-1a	2	76.37	0.39	0.07	0.01	13.04	0.42	0.95	0.01	0.05	0.01	0.04	0.00	0.69	0.18	4.01	0.03	4.77	0.21	0.01	0.01	100	0
Borrobol-type Tephra GI-1e_pt1	12	76.38	0.41	0.12	0.01	12.72	0.34	1.56	0.12	0.04	0.01	0.08	0.03	0.81	0.07	4.13	0.15	3.92	0.11	0.01	0.01	100	0
Borrobol-type Tephra GI-1e_pt2	8	76.42	0.59	0.12	0.01	12.64	0.57	1.47	0.15	0.04	0.01	0.07	0.02	0.84	0.04	4.16	0.20	3.99	0.07	0.01	0.00	100	0
Borrobol-type:GS-2.1-RHY Tephra	35	75.87	0.31	0.15	0.01	12.91	0.20	1.58	0.13	0.04	0.01	0.11	0.02	0.96	0.05	4.27	0.15	3.94	0.10	0.02	0.01	100	0
Borrobol Tephra	124	76.43	0.36	0.13	0.01	12.74	0.21	1.55	0.11	0.04	0.01	0.08	0.02	0.78	0.05	4.26	0.21	3.94	0.10	0.01	0.01	100	0
Borrobol Tephra-Sweden	13	76.50	0.25	0.13	0.01	12.76	0.23	1.53	0.10	0.04	0.01	0.07	0.02	0.77	0.05	4.24	0.32	3.96	0.13	0.01	0.00	100	0
Borrobol Tephra - ASHIK	21	76.46	0.54	0.12	0.02	12.92	0.48	1.36	0.16	0.04	0.01	0.06	0.03	0.70	0.10	4.21	0.51	3.95	0.10	0.01	0.01	100	0
CRUM1 597 Tephra	28	76.67	0.27	0.13	0.01	12.59	0.21	1.53	0.11	0.04	0.01	0.07	0.02	0.77	0.06	4.30	0.14	3.91	0.09	0.00	0.00	100	0
Fosen Tephra	10	76.88	0.49	0.12	0.01	12.31	0.43	1.54	0.20	0.05	0.00	0.07	0.03	0.70	0.12	4.30	0.14	4.02	0.30	0.01	0.00	100	0
Hasseldalen Tephra	104	77.75	0.50	0.09	0.02	12.15	0.32	1.14	0.14	0.04	0.02	0.03	0.02	0.51	0.09	4.03	0.27	4.22	0.24	0.01	0.02	100	0
Hovsdalur Tephra	23	77.88	0.53	0.12	0.02	12.27	0.12	1.16	0.12	0.04	0.02	0.05	0.02	0.49	0.06	3.61	0.24	4.38	0.60	0.01	0.00	100	0
Penifiler Tephra	171	76.78	0.49	0.12	0.02	12.77	0.31	1.36	0.20	0.04	0.02	0.05	0.03	0.69	0.10	4.10	0.40	4.02	0.12	0.01	0.02	100	0

Make TAS calculations

To calculate for the TAS plot the data require normalisation of the data. The rest of the script uses unnormalised data for plotting, but the code below can be used to normalise other data also by replacing the element to be normalise.

TAS_x <- (100/ chem [["Total"]]) * chem [["SiO2"]]
TAS_y <- (100/ chem [["Total"]]) * (chem [["K2O"]] + chem [["Na2O"]])

chem <- cbind(chem, TAS_x, TAS_y)

Filtering mixed datasets

It is possible your datasheet contains multiple types of tephra which would benefit from filtering before plotting. I suggest this is carried out by using silica values as a divisor. Rhyolites >69% silica is a case for this. However, it might be worth plotting everything first before then selecting a filter to ensure vital data is not being excluded.

chem <- filter(chem, SiO2 >68)

Plotting the data

First we generate a series of plots from all possible pairs of elements.

p1 <- ggplot(chem, aes(x = TAS_x, y = TAS_y, fill = id, shape = id))
p2 <- ggplot(chem, aes(x = SiO2, y = TiO2, fill = id, shape = id))
p3 <- ggplot(chem, aes(x = SiO2, y = Al2O3, fill = id, shape = id))
p4 <- ggplot(chem, aes(x = SiO2, y = FeO,  fill = id, shape = id))
p5 <- ggplot(chem, aes(x = SiO2, y = MnO,  fill = id, shape = id))
p6 <- ggplot(chem, aes(x = SiO2, y = MgO,  fill = id, shape = id))
p7 <- ggplot(chem, aes(x = SiO2, y = CaO,  fill = id, shape = id))
p8 <- ggplot(chem, aes(x = SiO2, y = Na2O,  fill = id, shape = id))
p9 <- ggplot(chem, aes(x = SiO2, y = K2O,  fill = id, shape = id))
p47 <- ggplot(chem, aes(x = SiO2, y = K2O, fill = id, shape = id))
p10 <- ggplot(chem, aes(x = SiO2, y = P2O5,  fill = id, shape = id))
p11 <- ggplot(chem, aes(x = TiO2, y = Al2O3,  fill = id, shape = id))
p12 <- ggplot(chem, aes(x = TiO2, y = FeO,  fill = id, shape = id))
p13 <- ggplot(chem, aes(x = TiO2, y = MnO,  fill = id, shape = id))
p14 <- ggplot(chem, aes(x = TiO2, y = MgO,  fill = id, shape = id))
p15 <- ggplot(chem, aes(x = TiO2, y = CaO,  fill = id, shape = id))
p16 <- ggplot(chem, aes(x = TiO2, y = Na2O,  fill = id, shape = id))
p17 <- ggplot(chem, aes(x = TiO2, y = K2O,  fill = id, shape = id))
p18 <- ggplot(chem, aes(x = TiO2, y = P2O5,  fill = id, shape = id))
p19 <- ggplot(chem, aes(x = Al2O3, y = FeO,  fill = id, shape = id))
p20 <- ggplot(chem, aes(x = Al2O3, y = MnO,  fill = id, shape = id))
p21 <- ggplot(chem, aes(x = Al2O3, y = MgO,  fill = id, shape = id))
p22 <- ggplot(chem, aes(x = Al2O3, y = CaO,  fill = id, shape = id))
p23 <- ggplot(chem, aes(x = Al2O3, y = Na2O,  fill = id, shape = id))
p24 <- ggplot(chem, aes(x = Al2O3, y = K2O,  fill = id, shape = id))
p25 <- ggplot(chem, aes(x = Al2O3, y = P2O5,  fill = id, shape = id))
p26 <- ggplot(chem, aes(x = FeO, y = MnO,  fill = id, shape = id))
p27 <- ggplot(chem, aes(x = FeO, y = MgO,  fill = id, shape = id))
p28 <- ggplot(chem, aes(x = FeO, y = CaO,  fill = id, shape = id))
p29 <- ggplot(chem, aes(x = FeO, y = Na2O,  fill = id, shape = id))
p30 <- ggplot(chem, aes(x = FeO, y = K2O,  fill = id, shape = id))
p31 <- ggplot(chem, aes(x = FeO, y = P2O5,  fill = id, shape = id))
p32 <- ggplot(chem, aes(x = MnO, y = MgO,  fill = id, shape = id))
p33 <- ggplot(chem, aes(x = MnO, y = CaO,  fill = id, shape = id))
p34 <- ggplot(chem, aes(x = MnO, y = Na2O,  fill = id, shape = id))
p35 <- ggplot(chem, aes(x = MnO, y = K2O,  fill = id, shape = id))
p36 <- ggplot(chem, aes(x = MnO, y = P2O5,  fill = id, shape = id))
p37 <- ggplot(chem, aes(x = MgO, y = CaO,  fill = id, shape = id))
p38 <- ggplot(chem, aes(x = MgO, y = Na2O,  fill = id, shape = id))
p39 <- ggplot(chem, aes(x = MgO, y = K2O,  fill = id, shape = id))
p40 <- ggplot(chem, aes(x = MgO, y = P2O5,  fill = id, shape = id))
p41 <- ggplot(chem, aes(x = CaO, y = Na2O,  fill = id, shape = id))
p42 <- ggplot(chem, aes(x = CaO, y = K2O,  fill = id, shape = id))
p43 <- ggplot(chem, aes(x = CaO, y = P2O5,  fill = id, shape = id))
p44 <- ggplot(chem, aes(x = Na2O, y = K2O,  fill = id, shape = id))
p45 <- ggplot(chem, aes(x = Na2O, y = P2O5,  fill = id, shape = id))
p46 <- ggplot(chem, aes(x = K2O, y = P2O5,  fill = id, shape = id))

Custom tephra theme

To ensure consistency and to provide a shortcut of coding it everytime we set up[ a custom tephra plotting theme. This means all of our plots will have the same sized fonts etc…

Tephra.theme <-theme_bw()+theme(axis.title.x=element_text(size=16),
                                axis.title.y=element_text(size=16), 
                                axis.text.x=element_text(size=16),
                                axis.text.y=element_text(size=16))+
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank())

Custom colour palette

The hex numbers below specify a custom colour palette for plotting. These can be altered but were slected in order to help those with colour vision deficiencies, and this should be considered when making changes. At present 35 layers can be plotted using this pallete. If you have more than 35 groups of data you will either have to specify additional colours or consider splitting your dataset. The latter might be more appropriate for improving the final clarity in the plots.

chempalette <- c("#209A24","#A769EE","#B4C428","#e30303","#000000","#EF139D","#3A700F","#5288FF","#E64701","#7F9AF5","#F36D20","#0B3075","#C18522","#9634A6","#1D7D46","#80ddb8","#7CBC92","#B10E89","#384B27","#F896FD","#C6A766","#20628E","#9C0323","#C3AFE0","#572D24","#FF79A2","#3E6D6F","#E18C63","#6F2B40","#DF9FB4", "#291700", "#7e005b", "#00597a" , "#7b003a",  "#470600")

The palette is known as “chempalette”.

Figures

We first generate the simple bi-plots of each element. This initial explorative approach facilitates data checking for unusual analyses and permits the identification of elements with significant variability.

TAS plot Le Maitre et al. (2002)

SiO2 VS TiO2

SiO2 VS Al2O3

SiO2 VS FeO

SiO2 VS MnO

SiO2 VS MgO

SiO2 VS CaO

SiO2 VS Na2O

SiO2 VS K2O

SiO2 VS P2O5

TiO2 VS Al2O3

TiO2 vs FeO

TiO2 vs MnO

TiO2 VS MgO

TiO2 VS CaO

TiO2 VS Na2O

TiO2 VS K2O

TiO2 VS P2O5

Al2O3 VS FeO

Al2O3 VS MnO

Al2O3 VS MgO

p21 <- p21 + geom_point(aes(fill = id), size=4, alpha=0.8)+xlab("Al2O3 (wt%)")+ylab("MgO (wt%)")+scale_shape_manual(values=c(21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25))+
  scale_fill_manual(values = chempalette) +
  Tephra.theme
p21

Al2O3 VS CaO

Al2O3 VS Na2O

Al2O3 VS K2O

Al2O3 VS P2O5

FeO VS MnO

FeO vs MgO

FeO vs CaO

FeO VS Na2O

FeO VS K2O

FeO VS P2O5

MnO VS MgO

MnO VS CaO

MnO VS Na2O

MnO VS K2O

MnO VS P2O5

MgO VS CaO

MgO VS Na2O

MgO VS K2O

MgO VS P2O5

CaO VS Na2O

CaO VS K2O

CaO VS P2O5

Na2O VS K2O

Na2O VS P2O5

K2O VS P2O5

# {-}

Saving individual plots

We propose exporting your plots in .svg format. This is a vector file format that preserves your data without size and scaling issues. It is also readable and editable by software like Adobe Illustrator, Affinity Designer, or free alternatives like Inkscape. .Using the file structure presented here the all plots can be named appropriately and will be saved in the Output -> Plots folder. The width and height and dpi outputs can be altered but override the preview functions in Rstudio and we recommend that you view the svg file directly in you preferred image editor to understand how it might look.

ggsave( filename = file.path("Script", "Output","Plots","fig1.svg"), plot = p1, width = 12, height = 6, dpi = 300)

Multivariate analysis

Multivariate analysis via ordination is often a helpful way of exploring your data. It permits the identification of useful elements for discrimination and facilitates dimension reduction. Unfortunately, percentage data can have problematic covariation and should be ‘freed’ from their compositional sum where possible. This can be achieved via undertaking a centred log-ratio transform.

centred log-ratio data table

chem_multi <- chem[ -c(1,6,11,12)] ## check columns to drop. Currently dropping id, MnO, P2O5, and Total to perform the clr transform.
chem_multi[is.na(chem_multi)] <- 0.005 # replace NAs with half the detection limit
chem_multi_clr <- clr(chem_multi)
cmclr <- as.data.frame(chem_multi_clr)
head(cmclr)

##      Sites   Country       age     SiO2    Al2O3        FeO       MnO       MgO
## 1 1.642765 0.3618315 0.3618315 3.093085 1.280336 -0.8536068 -4.394162 -3.397470
## 2 1.667643 0.3867093 0.3867093 3.117234 1.315663 -0.7952411 -4.167768 -3.906574
## 3 1.670452 0.3895177 0.3895177 3.118529 1.321894 -0.7552432 -4.170679 -3.650339
## 4 1.652841 0.3719074 0.3719074 3.100923 1.300696 -0.8593681 -4.363371 -3.507648
## 5 1.668021 0.3870871 0.3870871 3.116741 1.313589 -0.7739540 -4.443730 -3.871661
## 6 1.662949 0.3820153 0.3820153 3.110569 1.319112 -0.8551696 -4.348718 -3.800505
##         K2O      P2O5 Cl F    Total    TAS_x     TAS_y
## 1 0.1503695 -5.545292  0 0 3.357564 3.093085 0.8496628
## 2 0.1328745 -5.505815  0 0 3.382442 3.117234 0.8688885
## 3 0.1163958 -5.795532  0 0 3.385250 3.118529 0.8617065
## 4 0.1676347 -5.580336  0 0 3.367640 3.100923 0.8762521
## 5 0.1645933 -5.314697  0 0 3.382819 3.116741 0.8673628
## 6 0.1412168 -5.362589  0 0 3.377748 3.110569 0.8807886

We can summarise the importance of the resulting principal components by summarizing how much of the total variance is captured by each principal component.

PCA summary

Information	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12	PC13	PC14	PC15
Standard deviation	2.774224	2.161704	1.413388	0.7742391	0.6098498	0.5320467	0.2919113	0.2413936	0.1237158	0.04010732	0.0306669	0.02399163	0.001124607	8.722397e-16	7.458682e-17
Proportion of Variance	0.487620	0.296070	0.126570	0.0379800	0.0235600	0.0179400	0.0054000	0.0036900	0.0009700	0.00010000	0.0000600	0.00004000	0.000000000	0.000000e+00	0.000000e+00
Cumulative Proportion	0.487620	0.783690	0.910260	0.9482400	0.9718100	0.9897400	0.9951400	0.9988300	0.9998000	0.99990000	0.9999600	1.00000000	1.000000000	1.000000e+00	1.000000e+00

If this has captured a significant amount of variation it would be worth plotting the output in order to identify the important variables in determining the changes observed.

PCA scores scree plot

This is a visual representation of the summary table showing the percentage of the total variance explained by each dimension.

Variables PCA plot

Loadings dimension 1

Loadings dimension 2

PCA - Biplot

PCA - grouped

## Scale for 'shape' is already present. Adding another scale for 'shape', which
## will replace the existing scale.

Interactive figures

The function ggplotly within the plotly package is extremely useful for interactively examining data. ggplotly plots allow you to zoom into areas of interest and turn layers on and off as well as saving low resolution .png files of zoomed sections of your plots.We refer to these as iPlots in order to distinguish them from static plots presented above.

iTAS plot

iFeO-CaO

iK-lines

iTiO2 vs Al2O3

iK2O vs CaO

Saving all figures to a .pdf

l <- list(p1, p2, p3, p4, p5, p6, p7, p8, p9, p47, p10, p11, p12, p13, p14, p15, p16, p17, p18, p19, p20, p21, p22, p23, p24, p25, p26, p27, p28, p29, p30, p31, p32, p33, p34, p35, p36, p37, p38, p39, p40, p41, p42, p43, p44, p45, p46)
pdf("allplotsout.pdf", width = 12, height = 6)
invisible(lapply(l, print))
dev.off()

## png 
##   2

Harker plot

It is frequently useful to plot all of the collected elemental data against SiO[2]. This permits an assessme tof groupings within the data and which elements might be useful discriminators. Additionally, wt% K2O vs SiO2 can further classify the tephra to low, mid and high Potassium values. The dividing lines included here are from xxxxxxxx.

g1 <- plot_grid(p1 + theme(legend.position = "none"), 
                p2 + theme(legend.position = "none"),
                p3 + theme(legend.position="none"),
                p4 + theme(legend.position="none"),
                p5 + theme(legend.position="none"),
                p6 + theme(legend.position="none"),
                p7 + theme(legend.position="none"),
                p8 + theme(legend.position="none"), 
                p47 + theme(legend.position="none"), 
                p10 + theme(legend.position="none"),
                labels = c("A", "B", "C", "D", "E", "F", "G", "H", "I", "J"), align = "b")
legend <- get_legend(p1)
legplot <- plot_grid(legend)
px <- plot_grid( g1, rel_widths = c(4, 1))
l2 <- list(px,legplot)
pdf("Script/Output/Plots/Harkerplot.pdf", width = 30, height = 20)
invisible(lapply(l2, print))
dev.off()

## png 
##   2

ggsave( filename = file.path("Script","Output","Plots","Harker.svg"), plot = px, width = 30, height = 20, dpi = 300)

print(g1)

Other useful R tricks

Creating an inset within a figure

p100 <- ggplot(chem, aes(x = TAS_x, y = TAS_y, fill = id, shape = id))
p100 <- p100 + geom_point(aes(fill = id), size=4, alpha=0.8)+xlab("SiO2 (wt%)")+ylab("Na2O+K2O (wt%)")+
  scale_shape_manual(values=c(21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25))+
  scale_fill_manual(values = chempalette) +geom_segment(x=77,y=0,xend=69,yend=8)+ 
  geom_segment(x=69, y=8,xend=69,yend=13)+geom_segment(x=41, y=7,xend=52.5,yend=14)+
  geom_segment(x=52.5,y=14,xend=57.6,yend=11.7)+geom_segment(x=45,y=0,xend=45,yend=5)+
  geom_segment(x=45,y=5,xend=63,yend=14.56)+geom_segment(x=52,y=0,xend=52,yend=5)+
  geom_segment(x=52,y=5,xend=63,yend=7)+geom_segment(x=57,y=0,xend=57,yend=5.9)+
  geom_segment(x=63,y=0,xend=63,yend=7)+geom_segment(x=45,y=5,xend=52,yend=5)+
  geom_segment(x=52,y=5,xend=49.4,yend=7.3)+geom_segment(x=57,y=5.9,xend=53,yend=9.3)+
  geom_segment(x=63,y=7,xend=57.6,yend=11.7)+geom_segment(x=49.4,y=7.3,xend=45,yend=9.4)+
  geom_segment(x=53,y=9.3,xend=48.4,yend=11.5)+geom_segment(x=41,y=0,xend=41,yend=7)+
  geom_segment(x=52.5,y=14,xend=49,yend=15.5)+geom_segment(x=41,y=3,xend=45,yend=3)+
  geom_segment(x=63,y=7,xend=69,yend=8)+
  scale_x_continuous(limits=c(74, 79), expand = c(0, 0)) + ## set x axis of insert
  scale_y_continuous(limits=c(6, 10.5), expand = c(0, 0)) + ## set y axis of insert
  theme_bw()+theme(axis.title.x=element_text(size=9), axis.title.y=element_text(size=9), 
                   axis.text.x=element_text(size=9), axis.text.y=element_text(size=9))+ ## change the text size as neccessary
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.position = "none")
fig_in <- p1 + annotation_custom(ggplotGrob(p100), xmin = 40, xmax = 56, ymin = 7, ymax = 16) + ## place the insert
   annotate("rect", xmin = 74, xmax = 79, ymin = 6, ymax = 10.5,
  alpha = .2) ## use this to highlight the area of the insert plot

ggsave( filename = file.path("Script", "Output","Plots","insert_plot.svg"), plot = fig_in, width = 10, height = 6, dpi = 300)

print(fig_in)

## Creating a HDR plot to simplify your data. Simplifying the data we create to make more visually compelling figures can be acheived using highest density regions. The following code permits this as an addition to the previosuly created plots. It can be added easily to any of the previously created plots by changing the p code. Visibility of the density regions can be applied to just reference data or entire datasets. Opacity is varied using the alpha command and the probability range can be altered in the ‘probs’ argument. You may need to vary the x and y axes in order to fit the full range in to the plot.

p28den <- p28 +
geom_hdr(stat= "hdr", method = "kde", na.rm = TRUE, inherit.aes = TRUE, alpha  = 0.3, xlim = c(0,100), ylim = c(0,30), smooth = TRUE, n = 1000, probs = c(0.75))
print(p28den)

The plots are nice but messy and still have lots of overplotting. If you have particularly messy data I’d suggest creating plot ‘facets’ using the following code.

p28den_facet <- p28 +
geom_hdr(method = "kde", alpha  = 0.5, xlim = c(0,100), ylim = c(0,30), show.legend = FALSE, smooth = TRUE, n = 1000, probs = c(0.95)) +
  facet_wrap(~ id)


print(p28den_facet)

chem2 <- dplyr::select(chem, -Total)
chem2 <- rename(chem2, id2=id)
f19 <- ggplot(chem, aes(x = Al2O3, y = CaO,  fill = id, shape = id))

f19 <- f19 + 
    geom_point(data = chem2, inherit.aes = FALSE, aes(x=Al2O3, y = CaO, shape = id2), colour = "grey70") +
  geom_point(aes(fill = id), size=4, alpha=0.8)+xlab("Al2O3 (wt%)")+ylab("CaO (wt%)")+scale_shape_manual(values=c(21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25))+
  scale_fill_manual(values = chempalette) +
  Tephra.theme +
  facet_wrap(~ id)

ggsave( filename = file.path("Script", "Output","Plots","faceted plot.pdf"), plot = f19, width = 25, height = 16, units = "cm", dpi = 300)

print(f19)

unknowns <- here::here("Data","Ardtoe_16.5_B_type.csv") %>% read.csv()
gt(unknowns)

id2	SiO2	TiO2	Al2O3	FeO	MnO	MgO	CaO	Na2O	K2O	Cl	P2O5	Total
380	76.00313	0.13204995	12.90136	1.5669031	0.05215766	0.073890020	0.8094786	4.229427	4.206867	0	0.024940469	100
387	76.59048	0.12320307	12.46894	1.5890809	0.05106648	0.039752971	0.7755462	4.462193	3.900151	0	0.005189683	100
387	75.92839	0.11049529	13.42150	1.3561821	0.05105359	0.052917614	0.6484738	4.296372	4.118461	0	0.016258445	100
410	77.24444	0.11317187	11.92802	1.3273489	0.04997470	0.024466780	0.6460271	4.554777	4.095010	0	0.016658233	100
410	76.65740	0.14601109	12.19382	1.4629552	0.03929447	0.111457235	0.8955557	4.487471	3.965581	0	0.040453288	100
420	76.25817	0.14687726	12.63333	1.5402689	0.03212612	0.093858662	0.8514471	4.611148	3.811250	0	0.021627387	100
420	77.32078	0.10893876	12.62702	1.1756351	0.02867915	0.024266975	0.5681203	4.075696	4.062144	0	0.008824354	100
420	76.94974	0.11294698	12.32582	1.3819579	0.05475269	0.055065562	0.6674614	4.356958	4.079858	0	0.015330753	100
420	77.23369	0.10020921	11.99594	1.4343671	0.03381673	0.025853769	0.6393120	4.414686	4.112197	0	0.010031262	100
420	76.05568	0.10702770	12.62792	1.5104649	0.04185324	0.086829867	0.7079653	4.610416	4.240192	0	0.011764718	100
420	75.52786	0.16410136	13.05794	1.6359643	0.03976302	0.097514075	0.9487373	4.601044	3.897197	0	0.029980055	100
420	76.54834	0.12191103	12.38466	1.3845089	0.04515610	0.076546362	0.6711885	4.759640	3.979889	0	0.028157381	100
420	76.39237	0.15275863	12.77143	1.5077351	0.06188900	0.097238986	0.8168712	4.433079	3.745612	0	0.021018907	100
442	77.21185	0.10646580	12.22102	1.2313233	0.03903392	0.071260827	0.7180326	4.497622	3.883715	0	0.019782857	100
442	76.69401	0.10237887	12.23788	1.3744285	0.03858815	0.053542372	0.7210442	4.747284	4.009403	0	0.021542439	100
442	76.47075	0.11102017	12.70541	1.1456694	0.04764003	0.067577494	0.6471280	4.583412	4.204076	0	0.017314109	100
442	76.74392	0.11867632	12.73449	1.1364819	0.03539469	0.014157877	0.5949431	4.177302	4.424961	0	0.019779387	100
454	75.76463	0.13949886	12.82045	1.5436909	0.05801250	0.074144316	0.7587125	4.976045	3.837304	0	0.027506817	100
454	75.46961	0.12623091	13.23725	1.6791525	0.04617695	0.102673364	0.8101293	4.674400	3.828935	0	0.025538045	100
454	75.56446	0.14010431	12.78176	1.7300202	0.05034506	0.095329786	0.8687938	4.773006	3.975683	0	0.020390274	100
454	76.31437	0.13383242	12.64378	1.5893251	0.05717821	0.089464629	0.8199709	4.441466	3.884265	0	0.026245735	100
454	75.97227	0.14575976	12.91750	1.5434188	0.03583741	0.091270132	0.8381972	4.493089	3.951337	0	0.011526653	100
454	75.98706	0.14838109	13.24833	1.4673473	0.03240626	0.109201816	0.8817421	4.331603	3.768921	0	0.025008049	100
454	76.04285	0.15673637	12.86868	1.6107275	0.04817031	0.079308605	0.7418855	4.458836	3.981417	0	0.011494001	100
475	77.17689	0.11083083	12.21404	1.3087988	0.02836931	0.064783636	0.7981513	4.204691	4.080099	0	0.013443663	100
475	76.62986	0.11424276	12.72582	1.2411494	0.03463182	0.020905791	0.6802829	4.507521	4.027532	0	0.018160586	100
475	76.14597	0.16576605	12.68678	1.4035489	0.04758683	0.112716714	0.9232686	4.548167	3.944770	0	0.021324784	100
480	75.72011	0.13144165	13.14479	1.4961183	0.04974306	0.113033613	0.8535951	4.578947	3.895368	0	0.016960213	100
480	75.52408	0.10334690	13.10485	1.4388347	0.02739943	0.048548039	0.6299785	5.011074	4.104393	0	0.007605165	100
480	76.60738	0.11195173	12.47452	1.5198642	0.05286032	0.066568700	0.7041120	4.360925	4.085303	0	0.016616212	100
505-510	76.65605	0.09946791	12.89805	1.1464222	0.02530779	0.025722672	0.5703588	4.287181	4.276187	0	0.015350627	100
505-510	76.71721	0.09672477	12.37239	1.2699973	0.03887854	0.039612095	0.5796151	4.449968	4.429743	0	0.005868459	100
505-510	76.05787	0.14955098	12.52087	1.7823156	0.04245861	0.093955429	0.8774428	4.393625	4.065622	0	0.016289812	100
505-510	76.24703	0.14635568	12.82195	1.6703978	0.04826290	0.117941159	0.8152461	4.284743	3.835020	0	0.013058144	100
505-510	75.73803	0.14102717	12.70740	1.7781055	0.03686645	0.120984282	0.8963222	4.742584	3.830373	0	0.008307934	100
505-510	76.85715	0.11324408	12.43627	1.2549926	0.02068385	0.003516254	0.5168894	4.387768	4.390457	0	0.019029141	100
505-510	77.57019	0.10721989	11.96031	1.0596218	0.02662156	0.026516747	0.5913339	4.564025	4.078863	0	0.015406964	100
505-510	76.75269	0.10856079	13.10689	0.9835177	0.03759059	0.008319228	0.5732051	4.347156	4.061119	0	0.020952131	100
505-510	76.90239	0.12989825	12.50208	1.3284093	0.03730896	0.050445921	0.7178034	3.868151	4.447649	0	0.015869446	100
505-510	77.16865	0.11003945	12.48262	1.3499987	0.04051881	0.045858496	0.7008602	3.895355	4.192597	0	0.013610969	100
505-510	76.27339	0.10607059	12.78489	1.3079264	0.04881953	0.019569451	0.6909682	4.674601	4.078669	0	0.014885274	100
505-510	77.51055	0.09643494	12.04243	1.3480998	0.03696149	0.035600305	0.6335807	4.122411	4.162199	0	0.011727159	100
505-510	76.83687	0.09935218	12.23137	1.3357812	0.04441136	0.074435944	0.6581430	4.373894	4.322289	0	0.023352453	100
505-510	76.88847	0.10963497	12.17229	1.3376924	0.03654499	0.042271412	0.6928972	4.684943	4.021927	0	0.013535181	100
505-510	76.89560	0.09150607	12.56363	1.1051038	0.03174278	0.004756247	0.6305130	4.479868	4.181465	0	0.015716296	100
505-510	77.18127	0.11148237	12.42395	0.9811715	0.02153637	0.023647775	0.5991122	4.429524	4.223134	0	0.004961810	100
520-524	76.46034	0.11591999	12.34764	1.4231522	0.04443073	0.076753566	0.6535108	4.732294	4.123320	0	0.022531225	100
520-524	77.19453	0.11599690	12.73675	1.2753384	0.04936929	0.063071355	0.6914838	3.803527	4.056754	0	0.012969897	100
520-524	77.29433	0.10890360	11.97877	1.4754344	0.02764476	0.029634346	0.5058782	4.020428	4.541804	0	0.017173260	100
520-524	77.35520	0.10626971	11.70494	1.2323338	0.04144830	0.027112799	0.5122345	4.537394	4.475274	0	0.007894915	100
520-524	77.95664	0.11758516	11.93040	1.2341688	0.02620047	0.037821642	0.5859185	4.178024	3.918977	0	0.014367998	100
520-524	76.54875	0.10491271	12.84308	1.3202392	0.04131332	0.030380535	0.6189640	4.446491	4.033673	0	0.012404509	100
520-524	75.54970	0.14337933	12.87527	1.7624238	0.05149390	0.102670599	0.9275247	4.751079	3.822709	0	0.013745806	100
520-524	75.92387	0.14512247	12.74070	1.7208580	0.04616577	0.125352159	0.9112219	4.484389	3.886022	0	0.016299987	100
520-524	75.96993	0.15329138	12.74084	1.6926837	0.03009401	0.106896440	0.9011485	4.511803	3.881396	0	0.011703227	100
520-524	77.27263	0.11252922	12.13558	1.2587257	0.04014444	0.029016316	0.6558311	4.393632	4.091925	0	0.009984109	100
575-579	76.04575	0.15342392	12.63895	1.5826292	0.04766769	0.094716315	0.8835031	4.670918	3.867686	0	0.014754284	100
575-579	75.96736	0.14624645	12.64964	1.7403431	0.04519965	0.097915179	0.8515907	4.646900	3.823598	0	0.031316155	100
605-609	76.23718	0.14228793	13.05039	1.5928460	0.02999413	0.085562195	0.8694087	4.190548	3.789048	0	0.012629106	100
605-609	76.49533	0.13253017	12.85034	1.4540181	0.04650739	0.083903993	0.8073641	4.090743	4.002178	0	0.037078785	100
605-609	76.45414	0.06636754	12.85458	1.0322203	0.05258918	0.075412878	0.8326970	4.011607	4.594296	0	0.026189409	100
605-609	76.51119	0.13518393	12.34711	1.6006652	0.04506131	0.112497177	0.8646985	4.492810	3.870798	0	0.019980997	100
605-609	76.83272	0.13504035	12.65311	1.5101873	0.04592211	0.109353327	0.9304993	3.969117	3.795598	0	0.018452719	100
605-609	76.16139	0.12978653	12.58511	1.5135492	0.04984555	0.100736084	0.8987874	4.658730	3.878862	0	0.023094061	100
605-609	76.23662	0.13762903	12.75444	1.4864571	0.03350599	0.104653197	0.8986179	4.328104	3.993787	0	0.026083776	100
655-659	76.47049	0.12827809	12.42383	1.6668869	0.03693327	0.087807547	0.7680039	4.594291	3.809017	0	0.014565233	100
655-659	75.65204	0.13396388	12.84060	1.7914653	0.03577266	0.092211631	0.8448012	4.766471	3.837011	0	0.005769784	100
655-659	75.27886	0.15208143	13.58467	1.3514673	0.03711573	0.110416699	0.8980145	4.567302	3.987304	0	0.032773496	100
655-659	76.59546	0.12812480	12.34196	1.6668693	0.04998218	0.074401747	0.8927170	4.189670	4.041179	0	0.019639567	100
655-659	75.96815	0.14878880	13.20294	1.4516593	0.04544289	0.073085561	0.8291756	4.398222	3.862226	0	0.020313179	100
655-659	76.17420	0.13646579	13.12513	1.6005517	0.03745506	0.071154179	0.7814440	4.169614	3.877798	0	0.026395905	100
655-659	76.51764	0.13979448	12.83405	1.5541126	0.04818713	0.099897615	0.8837416	4.085129	3.817975	0	0.019585736	100
655-659	76.50526	0.14789827	12.54891	1.5670923	0.03881018	0.119682218	0.8965153	4.335098	3.820600	0	0.019929554	100
655-659	76.06042	0.15925514	12.67322	1.4786484	0.05235356	0.103766635	0.9430955	4.498749	4.009802	0	0.020795126	100
795-799	75.33530	0.13666351	13.58413	1.5086099	0.03983466	0.087942663	0.8680891	4.492328	3.939545	0	0.007354090	100
795-799	75.98120	0.13920379	12.90177	1.5876109	0.02271439	0.080750700	0.8788802	4.534543	3.854465	0	0.018859196	100
795-799	76.02033	0.14636837	12.72681	1.6261987	0.04571390	0.090064775	0.8310284	4.535322	3.953833	0	0.024429677	100
795-799	76.09458	0.15805704	12.89237	1.7420794	0.03933120	0.124583675	0.9393463	4.105508	3.875692	0	0.028452359	100
795-799	76.11842	0.13950288	12.91580	1.5388064	0.05046292	0.107181583	0.8208566	4.333660	3.969263	0	0.006151472	100
795-799	76.56116	0.13616181	12.37317	1.6105731	0.03365097	0.106769143	0.8507257	4.305871	4.007686	0	0.014332823	100
795-799	77.44136	0.13002308	12.22340	1.5432906	0.04673831	0.071960517	0.8779389	3.626028	4.011361	0	0.028001804	100
855-859	76.52489	0.14146244	12.69460	1.5045983	0.04330911	0.102033326	0.8832751	4.261071	3.824834	0	0.020029152	100
855-859	76.32537	0.14330571	13.02532	1.4287049	0.03301339	0.107320056	0.8976245	4.112343	3.908849	0	0.018152057	100
855-859	76.57861	0.14483409	12.49259	1.5230940	0.03135476	0.100916660	0.9157252	4.354056	3.829434	0	0.029278290	100

unknown_sum <- unknowns %>% group_by(id2) %>% dplyr::summarise(                                            
                                                       count=n(),
                                                       SiO2.= mean(SiO2),
                                                       dev_1 = sd(SiO2),
                                                       TiO2. = mean(TiO2),
                                                       dev_2 = sd(TiO2),
                                                       Al2O3. = mean(Al2O3),
                                                       dev_3 = sd(Al2O3),
                                                       FeOt = mean(FeO),
                                                       dev_4 = sd(FeO),
                                                       MnO.= mean(MnO),
                                                       dev_5 = sd(MnO),
                                                       MgO.= mean(MgO),
                                                       dev_6 = sd(MgO),
                                                       CaO.= mean(CaO),
                                                       dev_7 = sd(CaO),
                                                       Na2O. = mean(Na2O),
                                                       dev_8 = sd(Na2O),
                                                       K2O. = mean(K2O),
                                                       dev_9 = sd(K2O),
                                                       P2O5. = mean(P2O5),
                                                       dev_10 = sd(P2O5),
                                                       Total.=mean(Total),
                                                       dev_11 = sd(Total))
unknown_sum <- unknown_sum %>% mutate(across(where(is.numeric), ~ round(., digits = 2)))
unknown_sum %>% rename(layer = id2, n = count , SiO2 = SiO2., sd1 = dev_1, TiO2 = TiO2. , sd2 = dev_2)

## # A tibble: 15 x 24
##    layer       n  SiO2   sd1  TiO2   sd2 Al2O3. dev_3  FeOt dev_4  MnO. dev_5
##    <chr>   <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1 380         1  76   NA     0.13 NA      12.9 NA     1.57 NA     0.05 NA   
##  2 387         2  76.3  0.47  0.12  0.01   13.0  0.67  1.47  0.16  0.05  0   
##  3 410         2  77.0  0.42  0.13  0.02   12.1  0.19  1.4   0.1   0.04  0.01
##  4 420         8  76.5  0.61  0.13  0.02   12.6  0.32  1.45  0.14  0.04  0.01
##  5 442         4  76.8  0.31  0.11  0.01   12.5  0.28  1.22  0.11  0.04  0.01
##  6 454         7  75.9  0.29  0.14  0.01   12.9  0.23  1.59  0.09  0.05  0.01
##  7 475         3  76.6  0.52  0.13  0.03   12.5  0.28  1.32  0.08  0.04  0.01
##  8 480         3  76.0  0.58  0.12  0.01   12.9  0.38  1.48  0.04  0.04  0.01
##  9 505-510    16  76.8  0.5   0.11  0.02   12.5  0.31  1.31  0.25  0.04  0.01
## 10 520-524    10  76.8  0.78  0.12  0.02   12.4  0.44  1.44  0.21  0.04  0.01
## 11 575-579     2  76.0  0.06  0.15  0.01   12.6  0.01  1.66  0.11  0.05  0   
## 12 605-609     7  76.4  0.23  0.13  0.03   12.7  0.23  1.46  0.19  0.04  0.01
## 13 655-659     9  76.1  0.45  0.14  0.01   12.8  0.4   1.57  0.13  0.04  0.01
## 14 795-799     7  76.2  0.65  0.14  0.01   12.8  0.44  1.59  0.08  0.04  0.01
## 15 855-859     3  76.5  0.13  0.14  0      12.7  0.27  1.49  0.05  0.04  0.01
## # ... with 12 more variables: MgO. <dbl>, dev_6 <dbl>, CaO. <dbl>, dev_7 <dbl>,
## #   Na2O. <dbl>, dev_8 <dbl>, K2O. <dbl>, dev_9 <dbl>, P2O5. <dbl>,
## #   dev_10 <dbl>, Total. <dbl>, dev_11 <dbl>

gt(unknown_sum)

id2	count	SiO2.	dev_1	TiO2.	dev_2	Al2O3.	dev_3	FeOt	dev_4	MnO.	dev_5	MgO.	dev_6	CaO.	dev_7	Na2O.	dev_8	K2O.	dev_9	P2O5.	dev_10	Total.	dev_11
380	1	76.00	NA	0.13	NA	12.90	NA	1.57	NA	0.05	NA	0.07	NA	0.81	NA	4.23	NA	4.21	NA	0.02	NA	100	NA
387	2	76.26	0.47	0.12	0.01	12.95	0.67	1.47	0.16	0.05	0.00	0.05	0.01	0.71	0.09	4.38	0.12	4.01	0.15	0.01	0.01	100	0
410	2	76.95	0.42	0.13	0.02	12.06	0.19	1.40	0.10	0.04	0.01	0.07	0.06	0.77	0.18	4.52	0.05	4.03	0.09	0.03	0.02	100	0
420	8	76.54	0.61	0.13	0.02	12.55	0.32	1.45	0.14	0.04	0.01	0.07	0.03	0.73	0.13	4.48	0.21	3.99	0.17	0.02	0.01	100	0
442	4	76.78	0.31	0.11	0.01	12.47	0.28	1.22	0.11	0.04	0.01	0.05	0.03	0.67	0.06	4.50	0.24	4.13	0.24	0.02	0.00	100	0
454	7	75.87	0.29	0.14	0.01	12.93	0.23	1.59	0.09	0.05	0.01	0.09	0.01	0.82	0.05	4.59	0.23	3.89	0.08	0.02	0.01	100	0
475	3	76.65	0.52	0.13	0.03	12.54	0.28	1.32	0.08	0.04	0.01	0.07	0.05	0.80	0.12	4.42	0.19	4.02	0.07	0.02	0.00	100	0
480	3	75.95	0.58	0.12	0.01	12.91	0.38	1.48	0.04	0.04	0.01	0.08	0.03	0.73	0.11	4.65	0.33	4.03	0.12	0.01	0.01	100	0
505-510	16	76.77	0.50	0.11	0.02	12.50	0.31	1.31	0.25	0.04	0.01	0.05	0.04	0.67	0.11	4.37	0.25	4.16	0.19	0.01	0.00	100	0
520-524	10	76.75	0.78	0.12	0.02	12.40	0.44	1.44	0.21	0.04	0.01	0.06	0.04	0.70	0.16	4.39	0.30	4.08	0.25	0.01	0.00	100	0
575-579	2	76.01	0.06	0.15	0.01	12.64	0.01	1.66	0.11	0.05	0.00	0.10	0.00	0.87	0.02	4.66	0.02	3.85	0.03	0.02	0.01	100	0
605-609	7	76.42	0.23	0.13	0.03	12.73	0.23	1.46	0.19	0.04	0.01	0.10	0.01	0.87	0.04	4.25	0.26	3.99	0.28	0.02	0.01	100	0
655-659	9	76.14	0.45	0.14	0.01	12.84	0.40	1.57	0.13	0.04	0.01	0.09	0.02	0.86	0.06	4.40	0.23	3.90	0.09	0.02	0.01	100	0
795-799	7	76.22	0.65	0.14	0.01	12.80	0.44	1.59	0.08	0.04	0.01	0.10	0.02	0.87	0.04	4.28	0.33	3.94	0.06	0.02	0.01	100	0
855-859	3	76.48	0.13	0.14	0.00	12.74	0.27	1.49	0.05	0.04	0.01	0.10	0.00	0.90	0.02	4.24	0.12	3.85	0.05	0.02	0.01	100	0

chem_red <- filter(chem, age == "WI"|age == "GS2")

c28 <- ggplot(chem_red, aes(x = FeO, y = CaO,  fill = id, shape = id))
c28 <- c28 + 
  geom_hdr(method = "kde", alpha  = 0.5, xlim = c(0,100), ylim = c(0,30), show.legend = FALSE, smooth = TRUE, n = 1000, probs = c(0.95)) +
   scale_fill_manual(values = chempalette) +
    geom_point(data = unknowns, inherit.aes = FALSE, aes(x=FeO, y = CaO, shape = id2, fill = id2), size=4, alpha=0.8) +
  xlab("FeO (wt%)") +
  ylab("CaO (wt%)") + 
   scale_fill_manual(values = chempalette) +
  scale_shape_manual(values=c(1:5,7,11, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25)) +
  Tephra.theme

## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.

print(c28)

u28 <- ggplot(chem_red, aes(x = FeO, y = CaO,  fill = id, shape = id))
u28 <- u28 + geom_point(aes(fill = id), size=4, alpha=0.8)+xlab("FeO (wt%)")+ylab("CaO (wt%)")+scale_shape_manual(values=c(21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25, 21:25))+
      geom_point(data = unknowns, inherit.aes = FALSE, aes(x=FeO, y = CaO, shape = id2, fill = id2), size=4, alpha=0.8) +
  scale_fill_manual(values = chempalette) +
  Tephra.theme

ggplotly(u28, width = 1000, height = 800)

References

R. W. Le Maitre (editor), A. Streckeisen, B. Zanettin, M. J. Le Bas, B. Bonin, P. Bateman, G. Bellieni, A. Dudek, S. Efremova, J. Keller, J. Lamere, P. A. Sabine, R. Schmid, H. Sorensen, and A. R. Woolley, Igneous Rocks: A Classification and Glossary of Terms, Recommendations of the International Union of Geological Sciences, Subcommission of the Systematics of Igneous Rocks. Cambridge University Press, 2002.